Towards High Data Rates in Dynamic Environments Using Hybrid Cell-Free Massive MIMO/Small-Cell System

Elhoushy, Salah; Hamouda, Walaa

doi:10.1109/lwc.2020.3021026

Cited by 19 publications

(14 citation statements)

References 10 publications

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“…However, due to the fact that the hardware cost and circuit power consumption linearly increase with the sampling rate, and exponentially with the number of quantization bits of the ADCs/DACs [ 20 , 21 , 22 , 23 ], they will become a bottleneck for the future cell-free massive MIMO systems. To resolve this issue effectively, several novel architectures, such as low-resolution ADCs/DACs and hybrid precoding aided cell-free massive MIMO were proposed, and much effort was made to understand the behaviors of low-resolution ADCs/DACs [ 24 , 25 , 26 , 27 , 28 ]. Leveraging on the additive quantization noise model (AQNM), in [ 25 ], the authors took the first attempt to introduce low-resolution ADCs in cell-free massive MIMO systems and derived the achievable rate expression.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Optimization for Downlink Cell-Free Massive MIMO System with Mixed DACs

Zhou

Zhang

Qiao

et al. 2021

Sensors

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This paper concentrates on the rate analysis and optimization for a downlink cell-free massive multi-input multi-output (MIMO) system with mixed digital-to-analog converters (DACs), where some of the access points (APs) use perfect-resolution DACs, while the others exploit low-resolution DACs to reduce hardware cost and power consumption. By using the additive quantization noise model (AQNM) and conjugate beamforming receiver, a tight closed-form rate expression is derived based on the standard minimum mean square error (MMSE) channel estimate technique. With the derived result, the effects of the number of APs, the downlink transmitted power, the number of DAC bits, and the proportion of the perfect DACs in the mixed-DAC architecture are conducted. We find that the achievable sum rate can be improved by increasing the proportion of the perfect DACs and deploying more APs. Besides, when the DAC resolution arrives at 5-bit, the system performance will invariably approach the case of perfect DACs, which indicates that we can use 5-bit DACs to substitute the perfect DACs. Thus, it can greatly reduce system hardware cost and power consumption. Finally, the weighted max–min power allocation scheme is proposed to guarantee that the users with high priority have a higher rate, while the others are served with the same rate. The simulation results prove the proposed scheme can be effectively solved by the bisection algorithm.

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Section: Introductionmentioning

confidence: 99%

Analysis and Optimization for Downlink Cell-Free Massive MIMO System with Mixed DACs

Zhou

Zhang

Qiao

et al. 2021

Sensors

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“…In this paper, the capacity of SCs to provide coverage in 5G/B5G wireless networks is explored under the umbrella of the IA paradigm. A fully distributed topology as in [20][21][22] is considered, where there are no macro base stations and a large number of APs act as small base stations to provide communication services to a much smaller number of MSs. The system is modeled as a downlink K-user interference channel network, where each MS receives data from a single AP [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…The system is modeled as a downlink K-user interference channel network, where each MS receives data from a single AP [6,7]. Moreover, each MS and its corresponding AP are considered an SC as in [20][21][22]. The transmission within an SC causes interference to the remaining users.…”

Section: Introductionmentioning

confidence: 99%

“…The transmission within an SC causes interference to the remaining users. We note that the network architecture is somewhat similar to that considered in the cell-free related literature (see [20][21][22]); however, the studied setup only requires for APs/MSs within a cluster to share their CSI whereas cell-free study requires the distribution of all users' data to all APs. Furthermore, we consider multiple antennas at the transmitter and receiver, thus enabling the incorporation of precoding/combining techniques at both sides to increase the achievable SE of the network.…”

Section: Introductionmentioning

confidence: 99%

“…Interference is only taken into account in the clustered-IA stage because considering the SINR to perform the AP/MS pair assignment and then to form the clusters would involve an iterative process with prohibitive complexity. Note that, in practical applications, the use of the SNR to decide the AP/MS pair is a common practice in the literature [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deployment of Clustered-Based Small Cells in Interference-Limited Dense Scenarios: Analysis, Design, and Trade-Offs

Villalonga

Riera-Palou

García

et al. 2021

Wireless Communications and Mobile Computing

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Network densification is one of the most promising solutions to address the high data rate demands in 5G and beyond (B5G) wireless networks while ensuring an overall adequate quality of service. In this scenario, most users experience significant interference levels from neighbouring mobile stations (MSs) and access points (APs) making the use of advanced interference management techniques mandatory. Clustered interference alignment (IA) has been widely proposed to manage the interference in densely deployed scenarios with a large number of users. Nonetheless, the setups considered in previous works are still far from the densification levels envisaged for 5G/B5G networks that are considered in this paper. Moreover, prior designs of clustered-IA systems relied on oversimplified channel models and/or enforced single-stream transmission. In this paper, we explore an ultradense deployment of small cells (SCs) to provide coverage in 5G/B5G wireless networks. A novel cluster design based on a size-restricted k -means algorithm to divide the SCs into different clusters is proposed taking into account path loss and shadowing effects, thus providing a more realistic solution than those available in the current literature. Unlike previous works, this clustering method can also cater for spatial multiplexing scenarios. Also, several design parameters such as the number of transmit antennas, multiplexed data streams, and deployed APs are analyzed in order to identify trade-offs between performance and complexity. The relationship between density of network elements per area unit and performance is investigated, thus allowing to illustrate that there is an optimal coverage area value over which the network resources should be distributed. Moreover, it is shown that the spectral-efficiency degradation due to the intercluster interference in ultradense networks (UDNs) points to the need of designing an interference management algorithm that accounts for both intracluster and intercluster interferences. Simulation results provide key insights for the deployment of small cells in interference-limited dense scenarios.

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The cell-free mMIMO networks: mathematical analysis and performance evaluation

et al. 2021

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Towards High Data Rates in Dynamic Environments Using Hybrid Cell-Free Massive MIMO/Small-Cell System

Cited by 19 publications

References 10 publications

Analysis and Optimization for Downlink Cell-Free Massive MIMO System with Mixed DACs

Analysis and Optimization for Downlink Cell-Free Massive MIMO System with Mixed DACs

Deployment of Clustered-Based Small Cells in Interference-Limited Dense Scenarios: Analysis, Design, and Trade-Offs

The cell-free mMIMO networks: mathematical analysis and performance evaluation

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